OpenRaiser/Intern-Atlas huggingface dataset:
Guiding Testing Activities by Predicting Defect-prone Parts Using Product and Inspection Metrics
Venue: arXiv cs.SE 2013
Year: 2013

Product metrics, such as size or complexity, are often used to identify defect-prone parts or to focus quality assurance activities. In contrast, quality information that is available early, such as information provided by inspections, is usually not used. Currently, only little experience is documented in the literature on whether data from early defect detection activities can support the identification of defect-prone parts later in the development process. This article compares selected product and inspection metrics commonly used to predict defect-prone parts. Based on initial experience from two case studies performed in different environments, the suitability of different metrics for predicting defect-prone parts is illustrated. These studies revealed that inspection defect data seems to be a suitable predictor, and a combination of certain inspection and product metrics led to the best prioritizations in our contexts.
On sufficient conditions for expressibility of constants in the 4-valued extension of the propositional provability logic $GL$
Venue: arXiv cs.LO 2013
Year: 2013

In the present paper we consider the simplest non-classical extension $GL4$ of the well-known propositional provability logic $GL$ together with the notion of expressibility of formulas in a logic proposed by A. V. Kuznetsov. Conditions for expressibility of constants in the 4-valued extension $L\mathfrak{B}_2$ of $GL$ are found out, which were first announced in a author's paper in 1996.
On the Throughput of Hybrid-ARQ under QoS Constraints
Venue: arXiv cs.IT 2013
Year: 2013

Hybrid Automatic Repeat Request (HARQ) is a high performance communication protocol, leading to effective use of the wireless channel and the resources with only limited feedback about the channel state information (CSI) to the transmitter. In this paper, the throughput of HARQ with incremental redundancy (IR) and fixed transmission rate is studied in the presence of quality of service (QoS) constraints imposed as limitations on buffer overflow probabilities. In particular, tools from the theory of renewal processes and stochastic network calculus are employed to characterize the maximum arrival rates that can be supported by the wireless channel when HARQ-IR is adopted. Effective capacity is employed as the throughput metric and a closed-form expression for the effective capacity of HARQ-IR is determined for small values of the QoS exponent. The impact of the fixed transmission rate, QoS constraints, and hard deadline limitations on the throughput is investigated and comparisons with regular ARQ operation are provided.
Composite Channel Estimation in Massive MIMO Systems
Venue: arXiv cs.IT 2013
Year: 2013

We consider a multiuser (MU) multiple-input multiple-output (MIMO) time-division duplexing (TDD) system in which the base station (BS) is equipped with a large number of antennas for communicating with single-antenna mobile users. In such a system the BS has to estimate the channel state information (CSI) that includes large-scale fading coefficients (LSFCs) and small-scale fading coefficients (SSFCs) by uplink pilots. Although information about the former FCs are indispensable in a MU-MIMO or distributed MIMO system, they are usually ignored or assumed perfectly known when treating the MIMO CSI estimation problem. We take advantage of the large spatial samples of a massive MIMO BS to derive accurate LSFC estimates in the absence of SSFC information. With estimated LSFCs, SSFCs are then obtained using a rank-reduced (RR) channel model which in essence transforms the channel vector into a lower dimension representation. We analyze the mean squared error (MSE) performance of the proposed composite channel estimator and prove that the separable angle of arrival (AoA) information provided by the RR model is beneficial for enhancing the estimator's performance, especially when the angle spread of the uplink signal is not too large.
A Note on the Quantum Collision and Set Equality Problems
Venue: arXiv cs.CC 2013
Year: 2013

The results showing a quantum query complexity of $Θ(N^{1/3})$ for the collision problem do not apply to random functions. The issues are two-fold. First, the $Ω(N^{1/3})$ lower bound only applies when the range is no larger than the domain, which precludes many of the cryptographically interesting applications. Second, most of the results in the literature only apply to $r$-to-1 functions, which are quite different from random functions. Understanding the collision problem for random functions is of great importance to cryptography, and we seek to fill the gaps of knowledge for this problem. To that end, we prove that, as expected, a quantum query complexity of $Θ(N^{1/3})$ holds for all interesting domain and range sizes. Our proofs are simple, and combine existing techniques with several novel tricks to obtain the desired results. Using our techniques, we also give an optimal $Ω(N^{1/3})$ lower bound for the set equality problem. This new lower bound can be used to improve the relationship between classical randomized query complexity and quantum query complexity for so-called permutation-symmetric functions.
Routing of Physarum polycephalum signals using simple chemicals
Venue: arXiv cs.ET 2013
Year: 2013

In previous work the chemotaxis towards simple organic chemicals was assessed. We utilise the knowledge gained from these chemotactic assays to route Physarum polycephalum at a series of junctions. By applying chemical inputs at a simple T-junction we were able to reproducibly control the path taken by the plasmodium of P. Polycephalum. Where the chemoattractant farnesene was used at one input a routed signal could be reproducibly generated i.e. P. Polycephalum moves towards the source of chemoattractant. Where the chemoattractant was applied at both inputs the signal was reproducibly split. If a chemorepellent was used then the signal was reproducibly suppressed. If no chemical input was used in the simple circuit then a random signal was generated, whereby P. Polycephalum would move towards one output at the junction, but the direction was randomly selected. We extended this study to a more complex series of T-junctions to explore further the potential of routing P. Polycephalum. Although many of the circuits were completed effectively, any errors from the implementation of the simple T-junction were magnified. There were also issues with cascading effects through multiple junctions. For example signal splitting could be reproducibly initiated at the first junction but not at subsequent junctions. This work highlights the potential for exploiting chemotaxis to achieve complex and reliable routing of P. Polycephalum signals. This may be useful in implementing computing algorithms, design of autonomous robots and directed material synthesis. In additional experiments we showed that the application of chemoattractant compounds at specific locations on a homogeneous substrate could be used to reliably control the spatial configuration of P. Polycephalum. This may have applications in implementing geometric calculations and in robot navigation tasks such as mapping chemical plumes.
Algebraic Principles for Rely-Guarantee Style Concurrency Verification Tools
Venue: arXiv cs.LO 2013
Year: 2013

We provide simple equational principles for deriving rely-guarantee-style inference rules and refinement laws based on idempotent semirings. We link the algebraic layer with concrete models of programs based on languages and execution traces. We have implemented the approach in Isabelle/HOL as a lightweight concurrency verification tool that supports reasoning about the control and data flow of concurrent programs with shared variables at different levels of abstraction. This is illustrated on two simple verification examples.
Design and Analysis of an Estimation of Distribution Approximation Algorithm for Single Machine Scheduling in Uncertain Environments
Venue: arXiv cs.DS 2013
Year: 2013

In the current work we introduce a novel estimation of distribution algorithm to tackle a hard combinatorial optimization problem, namely the single-machine scheduling problem, with uncertain delivery times. The majority of the existing research coping with optimization problems in uncertain environment aims at finding a single sufficiently robust solution so that random noise and unpredictable circumstances would have the least possible detrimental effect on the quality of the solution. The measures of robustness are usually based on various kinds of empirically designed averaging techniques. In contrast to the previous work, our algorithm aims at finding a collection of robust schedules that allow for a more informative decision making. The notion of robustness is measured quantitatively in terms of the classical mathematical notion of a norm on a vector space. We provide a theoretical insight into the relationship between the properties of the probability distribution over the uncertain delivery times and the robustness quality of the schedules produced by the algorithm after a polynomial runtime in terms of approximation ratios.
High Performance Human Face Recognition using Gabor based Pseudo Hidden Markov Model
Venue: arXiv cs.CV 2013
Year: 2013

This paper introduces a novel methodology that combines the multi-resolution feature of the Gabor wavelet transformation (GWT) with the local interactions of the facial structures expressed through the Pseudo Hidden Markov model (PHMM). Unlike the traditional zigzag scanning method for feature extraction a continuous scanning method from top-left corner to right then top-down and right to left and so on until right-bottom of the image i.e. a spiral scanning technique has been proposed for better feature selection. Unlike traditional HMMs, the proposed PHMM does not perform the state conditional independence of the visible observation sequence assumption. This is achieved via the concept of local structures introduced by the PHMM used to extract facial bands and automatically select the most informative features of a face image. Thus, the long-range dependency problem inherent to traditional HMMs has been drastically reduced. Again with the use of most informative pixels rather than the whole image makes the proposed method reasonably faster for face recognition. This method has been successfully tested on frontal face images from the ORL, FRAV2D and FERET face databases where the images vary in pose, illumination, expression, and scale. The FERET data set contains 2200 frontal face images of 200 subjects, while the FRAV2D data set consists of 1100 images of 100 subjects and the full ORL database is considered. The results reported in this application are far better than the recent and most referred systems.
Beating the Generator-Enumeration Bound for $p$-Group Isomorphism
Venue: arXiv cs.DS 2013
Year: 2013

We consider the group isomorphism problem: given two finite groups G and H specified by their multiplication tables, decide if G cong H. For several decades, the n^(log_p n + O(1)) generator-enumeration bound (where p is the smallest prime dividing the order of the group) has been the best worst-case result for general groups. In this work, we show the first improvement over the generator-enumeration bound for p-groups, which are believed to be the hard case of the group isomorphism problem. We start by giving a Turing reduction from group isomorphism to n^((1 / 2) log_p n + O(1)) instances of p-group composition-series isomorphism. By showing a Karp reduction from p-group composition-series isomorphism to testing isomorphism of graphs of degree at most p + O(1) and applying algorithms for testing isomorphism of graphs of bounded degree, we obtain an n^(O(p)) time algorithm for p-group composition-series isomorphism. Combining these two results yields an algorithm for p-group isomorphism that takes at most n^((1 / 2) log_p n + O(p)) time. This algorithm is faster than generator-enumeration when p is small and slower when p is large. Choosing the faster algorithm based on p and n yields an upper bound of n^((1 / 2 + o(1)) log n) for p-group isomorphism.
Special Algorithm for Stability Analysis of Multistable Biological Regulatory Systems
Venue: arXiv cs.SC 2013
Year: 2013

We consider the problem of counting (stable) equilibriums of an important family of algebraic differential equations modeling multistable biological regulatory systems. The problem can be solved, in principle, using real quantifier elimination algorithms, in particular real root classification algorithms. However, it is well known that they can handle only very small cases due to the enormous computing time requirements. In this paper, we present a special algorithm which is much more efficient than the general methods. Its efficiency comes from the exploitation of certain interesting structures of the family of differential equations.
Flexible queries in XML native databases
Venue: arXiv cs.IR 2013
Year: 2013

To date, most of the XML native databases (DB) flexible querying systems are based on exploiting the tree structure of their semi structured data (SSD). However, it becomes important to test the efficiency of Formal Concept Analysis (FCA) formalism for this type of data since it has been proved a great performance in the field of information retrieval (IR). So, the IR in XML databases based on FCA is mainly based on the use of the lattice structure. Each concept of this lattice can be interpreted as a pair (response, query). In this work, we provide a new flexible modeling of XML DB based on fuzzy FCA as a first step towards flexible querying of SSD.
From Maxout to Channel-Out: Encoding Information on Sparse Pathways
Venue: arXiv cs.NE 2013
Year: 2013

Motivated by an important insight from neural science, we propose a new framework for understanding the success of the recently proposed "maxout" networks. The framework is based on encoding information on sparse pathways and recognizing the correct pathway at inference time. Elaborating further on this insight, we propose a novel deep network architecture, called "channel-out" network, which takes a much better advantage of sparse pathway encoding. In channel-out networks, pathways are not only formed a posteriori, but they are also actively selected according to the inference outputs from the lower layers. From a mathematical perspective, channel-out networks can represent a wider class of piece-wise continuous functions, thereby endowing the network with more expressive power than that of maxout networks. We test our channel-out networks on several well-known image classification benchmarks, setting new state-of-the-art performance on CIFAR-100 and STL-10, which represent some of the "harder" image classification benchmarks.
Region and Location Based Indexing and Retrieval of MR-T2 Brain Tumor Images
Venue: arXiv cs.CV 2013
Year: 2013

In this paper, region based and location based retrieval systems have been implemented for retrieval of MR-T2 axial 2-D brain images. This is done by extracting and characterizing the tumor portion of 2-D brain slices by use of a suitable threshold computed over the entire image. Indexing and retrieval is then performed by computing texture features based on gray-tone spatial-dependence matrix of segmented regions. A Hash structure is used to index all images. A combined index is adopted to point to all similar images in terms of the texture features. At query time, only those images that are in the same hash bucket as those of the queried image are compared for similarity, thus reducing the search space and time.
Code Coverage Based Test Case Selection and Prioritization
Venue: arXiv cs.SE 2013
Year: 2013

Regression Testing is exclusively executed to guarantee the desirable functionality of existing software after pursuing quite a few amendments or variations in it. Perhaps, it testifies the quality of the modified software by concealing the regressions or software bugs in both functional and non-functional applications of the system. In fact, the maintenance of test suite is enormous as it necessitates a big investment of time and money on test cases on a large scale. So, minimizing the test suite becomes the indispensable requisite to lessen the budget on regression testing. Precisely, this research paper aspires to present an innovative approach for the effective selection and prioritization of test cases which in return may procure a maximum code average.
Robust Subspace System Identification via Weighted Nuclear Norm Optimization
Venue: arXiv cs.SY 2013
Year: 2013

Subspace identification is a classical and very well studied problem in system identification. The problem was recently posed as a convex optimization problem via the nuclear norm relaxation. Inspired by robust PCA, we extend this framework to handle outliers. The proposed framework takes the form of a convex optimization problem with an objective that trades off fit, rank and sparsity. As in robust PCA, it can be problematic to find a suitable regularization parameter. We show how the space in which a suitable parameter should be sought can be limited to a bounded open set of the two dimensional parameter space. In practice, this is very useful since it restricts the parameter space that is needed to be surveyed.
End-to-end Phoneme Sequence Recognition using Convolutional Neural Networks
Venue: arXiv cs.LG 2013
Year: 2013

Most phoneme recognition state-of-the-art systems rely on a classical neural network classifiers, fed with highly tuned features, such as MFCC or PLP features. Recent advances in ``deep learning'' approaches questioned such systems, but while some attempts were made with simpler features such as spectrograms, state-of-the-art systems still rely on MFCCs. This might be viewed as a kind of failure from deep learning approaches, which are often claimed to have the ability to train with raw signals, alleviating the need of hand-crafted features. In this paper, we investigate a convolutional neural network approach for raw speech signals. While convolutional architectures got tremendous success in computer vision or text processing, they seem to have been let down in the past recent years in the speech processing field. We show that it is possible to learn an end-to-end phoneme sequence classifier system directly from raw signal, with similar performance on the TIMIT and WSJ datasets than existing systems based on MFCC, questioning the need of complex hand-crafted features on large datasets.
Sequential Monte Carlo Inference of Mixed Membership Stochastic Blockmodels for Dynamic Social Networks
Venue: arXiv cs.SI 2013
Year: 2013

Many kinds of data can be represented as a network or graph. It is crucial to infer the latent structure underlying such a network and to predict unobserved links in the network. Mixed Membership Stochastic Blockmodel (MMSB) is a promising model for network data. Latent variables and unknown parameters in MMSB have been estimated through Bayesian inference with the entire network; however, it is important to estimate them online for evolving networks. In this paper, we first develop online inference methods for MMSB through sequential Monte Carlo methods, also known as particle filters. We then extend them for time-evolving networks, taking into account the temporal dependency of the network structure. We demonstrate through experiments that the time-dependent particle filter outperformed several baselines in terms of prediction performance in an online condition.
Solve of problems of mathematical theory of learning with using computer modeling methods
Venue: arXiv cs.OH 2013
Year: 2013

Analyzed models of learning, which take into account that: 1) the rate of increase of student's knowledge is proportional to the difference between levels of teacher's requirements and prior knowledge; 2) if the requirements are too high, then student motivation decreases and he stops learning. Was proposed: 1) a one component model, coming from the fact that the training information consists of equal elements; 2) a two component model that takes into account that knowledge is assimilated with varying strength, 'trustworthy' knowledge forgotten much slower then 'weak'; 3) two component model, which takes into account the transition of 'weak' knowledge in 'trustworthy' knowledge. The solution of the five predictors and optimization problems of learning theory are represented.
Research on fresh agriculture product based on overconfidence of the retailer under options and spot markets dominated
Venue: arXiv cs.CE 2013
Year: 2013

In this article, we analyze the application of options contract in special commodity supply chain such as fresh agricultural products. This problem is discussed in the point of the retailer. When spot market and future market are both available, we discuss how the retailer chooses the optimal production. Furthermore, overconfidence is introduced to the supply chain of the fresh agricultural products, which has not happened before. Then,based on the overconfidence of the retailer, we explore how overconfidence affects the supply chain system under different circumstances. At last, we get the conclusion that different overconfidence level has different affection on retailer's optimal ordering quantity and profit.
ABMQ: An Agent-Based Modeler and Simulator for Self-Organization in MANETs using Qt
Venue: arXiv cs.NI 2013
Year: 2013

Agent-Based Modeling and Simulation (ABMS) is a simple and yet powerful method for simulation of interactions among individual agents. Using ABMS, different phenomena can be modeled and simulated without spending additional time on unnecessary complexities. Although ABMS is well-matured in many different fields such as economic, social, and natural phenomena, it has not received much attention in the context of mobile ad-hoc networks (MANETs). In this paper, we present ABMQ, a powerful Agent-Based platform suitable for modeling and simulation of self-organization in wireless networks, and particularly MANETs. By utilizing the unique potentials of Qt Application Framework, ABMQ provides the ability to easily model and simulate self-organizing algorithms, and then reuse the codes and models developed during simulation process for building real third-party applications for several desktop and mobile platforms, which substantially decreases the development time and cost, and prevents probable bugs that can happen as a result of rewriting codes.
Pattern Based Adaptive Architecture for Internet Banking
Venue: arXiv cs.SE 2013
Year: 2013

Pattern plays a vital role in software architecture and it is a general reusable solution to commonly occurring problem. Software architecture of a system is the set of structures needed to reason about the system, which comprise software elements, relations among them, and properties of both. Patterns can be implemented at run-time; they identify key resource constraints and best practices. Architecture Pattern expresses a fundamental structural organization or schema for software systems. Patterns in software architecture, offer the promise of helping the architect to identify combinations of Architecture or Solution Building Blocks that have been proven to deliver effective solutions. In Internet banking, we analyzed some attributes such as reliability, security, availability, load balancing and so on. The use of patterns, which is of a reusable component, is a good tool to help designers build load balancing systems. In this paper we are going to propose pattern based adaptive architecture for internet banking system and so the above attributes can be improved by the usage of patterns.